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The device is air pressure massager intended to temporarily relieve minor muscle aches and/or pains, and to temporarily increase circulation to the treated areas.

The system consists of an air pump, leg sleeves and hoses working together as one unit. The air pump is connected to the dedicated sleeves via a series of hoses; one sleeve has 4 compression chambers. The compression massage direction is from limb end to body center by inflating the air chambers sequentially and then deflating as one cycle, the pressure can be adjusted to avoid any discomfort to the patient. The sleeve works under the action of sensor and microprocessor. This software is to control the timing and the pressure reflected by the sensor, it cycles the airflow to reach the function of cycling compression of body parts.

The provided text describes a 510(k) premarket notification for the "Pressure Therapy System PT1003". This document focuses on demonstrating substantial equivalence to predicate devices rather than proving a predetermined set of acceptance criteria for a novel AI/imaging device. As such, many of the requested categories for AI/imaging device studies (like MRMC studies, effect size of AI assistance, expert qualifications, sample provenance, training sets, etc.) are not applicable or not provided in this regulatory submission.

However, I can extract information related to the performance characteristics, safety testing, and how the device's adherence to standards and safety was demonstrated.

Here's a breakdown of the available information:

1. A table of acceptance criteria and the reported device performance

The document doesn't explicitly state "acceptance criteria" in the format typically used for AI/imaging performance metrics (e.g., sensitivity, specificity, AUC). Instead, it demonstrates compliance with recognized medical device standards and functional tests to ensure safety and performance comparable to legally marketed predicate devices.

2. Sample size used for the test set and the data provenance

This document does not describe a test set in the context of clinical data for AI/imaging performance. The testing performed consists of bench testing and electrical/biocompatibility safety evaluations on the device itself. Therefore, concepts like "sample size" for a data set or "data provenance" (country of origin, retrospective/prospective) are not applicable. The tests were performed on the "PT1003" device following defined procedures.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. "Ground truth" for AI/imaging interpretation is not relevant for this type of device (a powered inflatable tube massager). The evaluation focuses on engineering, safety, and functional performance.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable. This is not a study requiring adjudication of expert interpretations.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. This is not an AI/imaging device.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Not applicable. This is not an AI/imaging device. The "standalone" performance here refers to the device's functional and safety performance in isolation, which was indeed assessed through the listed standards and function tests.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The "ground truth" for this device's evaluation is primarily established by regulatory standards (e.g., IEC, ISO, AAMI ANSI ES) and the functional and safety performance of legally marketed predicate devices. The device is deemed safe and effective if it meets these standards and its performance characteristics (like pressure range, cycle time) are comparable to or safely within the established parameters of already cleared devices.

8. The sample size for the training set

Not applicable. This is not an AI/imaging device, and therefore does not involve a "training set" in the machine learning sense.

9. How the ground truth for the training set was established

Not applicable. As above, no training set for AI was used.

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The Speed Hound ProPerformance Recovery System is indicated for the temporary relief of minor muscle aches and pains and for temporary increase in circulation to the treated areas in good health. The Speed Hound Recovery System simulates kneading and stroking of tissues by using an inflatable garment.

This submission is for the Speed Hound ProPerformance Recovery System. The Speed Hound ProPerformance Recovery System is a powered inflatable tube massager. It is intended to temporarily relieve minor muscle aches and pains, and to temporarily increase in circulation to the treated areas. It simulates manual kneading and stroking of tissues by use of an inflatable pressure cuff. The device is to be used by people who are in good health.

The device is a Class II, type BF applied part that receives power through a non-detachable power cord

The ProPerformance Recovery System consists of an air compressor unit with a control system, an inflatable "garment" (arms, legs and hips), plastic air tubing with a proprietary connector for connecting the device to the garment A description of each of these components is provided below.

The hardware and software of this device is identical to the model PT1003 by Xiamen Senyang Co., Ltd., cleared under 510(k) K181409

The "garments" (leg, arm and hip) and the plastic air tubing are of identical materials to the components of the FDA-cleared PT1003 (K181409).

The provided text is a 510(k) summary for the ProPerformance Recovery System, a powered inflatable tube massager. This device is seeking clearance based on substantial equivalence to a predicate device.

The context of this document is a regulatory submission for a medical device that does not involve an AI/ML algorithm. Therefore, many of the requested elements for describing the acceptance criteria and study proving an AI/ML device meets acceptance criteria are not applicable in this case. The device is a physical product, and its performance is assessed through bench testing and comparison to a legally marketed predicate, not through evaluation of an AI's diagnostic or predictive capabilities.

Here's an analysis based on the provided document, addressing the relevant points and explicitly stating N/A for those that are not applicable to this type of device:

Analysis of ProPerformance Recovery System Performance

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't present a formal table of "acceptance criteria" in the sense of accuracy metrics for an algorithm. Instead, it demonstrates performance by showing equivalence to a predicate device and compliance with recognized voluntary standards through bench testing.

2. Sample Size Used for the Test Set and the Data Provenance:

• Sample Size: Not applicable in the context of an AI/ML test set. The performance evaluation for this physical device relies on bench testing for compliance with electrical safety, EMC, and home use standards, and on direct comparison of specifications and features to a predicate device. The document mentions "Testing of all controls," "Testing of all indicators," and "Testing of performance" without specifying a sample size for these physical tests, which are typically product-level validations.
• Data Provenance: The data comes from internal "bench" performance testing conducted by the manufacturer/applicant to demonstrate compliance with voluntary standards (AAMI/ANSI ES60601-1, IEC 60601-1-2, IEC 60601-1-11). It is retrospective in the sense that the test results are submitted after completion. The country of origin of the data is implicitly the country where the testing was performed, likely related to the applicant's location (Menlo Park, CA) or their manufacturing partner (Xiamen Senyang Co., Ltd., China, for the identical hardware/software model).

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts:

• Not applicable. This pertains to AI/ML ground truth establishment. For a physical device, "ground truth" is typically defined by engineering specifications, regulatory standards, and physical measurements. The "experts" involved are likely engineers, quality assurance personnel, and regulatory professionals.

4. Adjudication Method for the Test Set:

• Not applicable. This applies to multi-reader evaluations in AI/ML studies. Performance validation here involves meeting predefined engineering and regulatory specifications through testing.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No. This is not an AI-assisted device. Therefore, no MRMC study was conducted, and no effect size for human reader improvement with AI assistance is applicable.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done:

• No. This device is a physical product, not an algorithm. Therefore, no standalone algorithm performance was assessed.

7. The Type of Ground Truth Used:

• For this device, the "ground truth" is based on:
  • Engineering Specifications: The device's design and operational parameters (e.g., pressure range, modes, dimensions).
  • Regulatory Standards: Compliance with recognized international and national consensus standards for medical electrical equipment (e.g., AAMI/ANSI ES60601-1, IEC 60601-1-2, IEC 60601-1-11).
  • Predicate Device Equivalence: The specifications and performance of the legally marketed predicate device (K182668 - Rapid Reboot Compression Therapy System) and a reference device (K181409 - Pressure Therapy System PT1003).

8. The Sample Size for the Training Set:

• Not applicable. This device does not involve a training set for an AI/ML algorithm.

9. How the Ground Truth for the Training Set was Established:

• Not applicable. As there is no AI/ML training set, this question is irrelevant.

In summary, the ProPerformance Recovery System is a conventional medical device. Its "acceptance criteria" are based on demonstrating substantial equivalence to a legally marketed predicate device and compliance with relevant safety and performance standards through bench testing, rather than through clinical studies or AI/ML performance evaluations.

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